Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline.

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the CZ ID AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

Doxycycline post-exposure prophylaxis for sexually transmitted infections impacts the gut antimicrobial resistome.

Doxycycline post-exposure prophylaxis (doxy-PEP) reduces bacterial sexually transmitted infections (STIs) among men who have sex with men and transgender women. While poised for widespread clinical implementation, the impact of doxy-PEP on antimicrobial resistance remains a primary concern as its effects on the gut microbiome and resistome, or the antimicrobial resistance genes (ARGs) present in the gut microbiome, are unknown. To investigate these effects, we studied participants from a randomized clinical trial who either received doxy-PEP as a one-time doxycycline 200 mg taken after condomless sex (DP arm, n = 100) or standard of care treatment (SOC arm, n = 50). From self-collected rectal swabs at enrollment (day-0) and after 6 months (month-6), we performed metagenomic DNA sequencing (DNA-seq) or metatranscriptomic RNA sequencing (RNA-seq). DNA-seq data was analyzable from 127 samples derived from 89 participants, and RNA-seq data from 86 samples derived from 70 participants. We compared the bacterial microbiome and resistome between the two study arms and over time. Tetracycline ARGs were detected in all day-0 DNA-seq samples and 85% of day-0 RNA-seq samples. The proportional mass of tetracycline ARGs in the resistome increased between day-0 and month-6 in DP participants from 46-51% in the metagenome (p = 0.02) and 4-15% in the metatranscriptome (p < 0.01), but no changes in other ARG classes were observed. Exposure to a higher number of doxycycline doses correlated with proportional enrichment of tetracycline ARGs in the metagenome (Spearman's ρ = 0.23, p < 0.01) and metatranscriptome (Spearman's ρ = 0.55, p < 0.01). Bacterial microbiome alpha diversity, beta diversity, and total bacterial mass did not differ between day-0 and month-6 samples from DP participants when assessed by either DNA-seq or RNA-seq. In an abundance-based correlation analysis, we observed an increase over time in the strength of the correlation between tetracycline ARGs and specific bacterial taxa, including some common human pathogens. In sum, doxy-PEP use over a 6-month period was associated with an increase in the proportion of tetracycline ARGs comprising the gut resistome, and an increase in the expression of tetracycline ARGs. Notably, doxy-PEP did not significantly alter alpha diversity or taxonomic composition of the gut microbiome, and did not demonstrate significant increases in non-tetracycline ARG classes. Further studies and population level surveillance are needed to understand the implications of these findings as doxy-PEP is implemented as a public health strategy.

Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline.

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

PoMeLo: a systematic computational approach to predicting metabolic loss in pathogen genomes.

BACKGROUND: Genome streamlining, the process by which genomes become smaller and encode fewer genes over time, is a common phenomenon among pathogenic bacteria. This reduction is driven by selection for minimized energy expenditure in a nutrient-rich environment. As pathogens evolve to become more reliant on the host, metabolic genes and resulting capabilities are lost in favor of siphoning metabolites from the host. Characterizing genome streamlining, gene loss, and metabolic pathway degradation can be useful in assessing pathogen dependency on host metabolism and identifying potential targets for host-directed therapeutics. RESULTS: PoMeLo (Predictor of Metabolic Loss) is a novel evolutionary genomics-guided computational approach for identifying metabolic gaps in the genomes of pathogenic bacteria. PoMeLo leverages a centralized public database of high-quality genomes and annotations and allows the user to compare an unlimited number of genomes across individual genes and pathways. PoMeLo runs locally using user-friendly prompts in a matter of minutes and generates tabular and visual outputs for users to compare predicted metabolic capacity between groups of bacteria and individual species. Each pathway is assigned a Predicted Metabolic Loss (PML) score to assess the magnitude of genome streamlining. Optionally, PoMeLo places the results in an evolutionary context by including phylogenetic relationships in visual outputs. It can also initially compute phylogenetically-weighted mean genome sizes to identify genome streamlining events. Here, we describe PoMeLo and demonstrate its use in identifying metabolic gaps in genomes of pathogenic Treponema species. CONCLUSIONS: PoMeLo represents an advance over existing methods for identifying metabolic gaps in genomic data, allowing comparison across large numbers of genomes and placing the resulting data in a phylogenetic context. PoMeLo is freely available for academic and non-academic use at https://github.com/czbiohub-sf/pomelo .

The antibiotic resistance reservoir of the lung microbiome expands with age in a population of critically ill patients.

Antimicrobial resistant lower respiratory tract infections are an increasing public health threat and an important cause of global mortality. The lung microbiome can influence susceptibility of respiratory tract infections and represents an important reservoir for exchange of antimicrobial resistance genes. Studies of the gut microbiome have found an association between age and increasing antimicrobial resistance gene burden, however, corollary studies in the lung microbiome remain absent. We performed an observational study of children and adults with acute respiratory failure admitted to the intensive care unit. From tracheal aspirate RNA sequencing data, we evaluated age-related differences in detectable antimicrobial resistance gene expression in the lung microbiome. Using a multivariable logistic regression model, we find that detection of antimicrobial resistance gene expression was significantly higher in adults compared with children after adjusting for demographic and clinical characteristics. This association remained significant after additionally adjusting for lung bacterial microbiome characteristics, and when modeling age as a continuous variable. The proportion of adults expressing beta-lactam, aminoglycoside, and tetracycline antimicrobial resistance genes was higher compared to children. Together, these findings shape our understanding of the lung resistome in critically ill patients across the lifespan, which may have implications for clinical management and global public health.

Building up a genomic surveillance platform for SARS-CoV-2 in the middle of a pandemic: a true North-South collaboration.

Next-generation sequencing technology has revolutionised pathogen surveillance over the last two decades. However, the benefits are not equitably distributed, with developing countries lagging far behind in acquiring the required technology and analytical capacity. Recent declines in the cost associated with sequencing-equipment and running consumables have created an opportunity for broader adoption. During the COVID-19 pandemic, rapid diagnostics development and DNA sequencing revolutionised the ability to diagnose and sequence SARS-CoV-2 rapidly. Socioeconomic inequalities substantially impact the ability to sequence SARS-CoV-2 strains and undermine a developing country's pandemic preparedness. Low- and middle-income countries face additional challenges in establishing, maintaining and expanding genomic surveillance. We present our experience of establishing a genomic surveillance system at the Aga Khan University, Karachi, Pakistan. Despite being at a leading health sciences research institute in the country, we encountered significant challenges. These were related to collecting standardised contextual data for SARS-CoV-2 samples, procuring sequencing reagents and consumables, and challenges with library preparation, sequencing and submission of high-quality SARS-CoV-2 genomes. Several technical roadblocks ensued during the implementation of the genomic surveillance framework, which were resolved in collaboration with our partners. High-quality genome sequences were then deposited on open-access platforms per the best practices. Subsequently, these efforts culminated in deploying Pakistan's first SARS-CoV-2 phyllo surveillance map as a Nextstrain build. Our experience offers lessons for the successful development of Genomic Surveillance Infrastructure in resource-limited settings struck by a pandemic.

The antibiotic resistance reservoir of the lung microbiome expands with age.

Antimicrobial resistant lower respiratory tract infections (LRTI) are an increasing public health threat, and an important cause of global mortality. The lung microbiome influences LRTI susceptibility and represents an important reservoir for exchange of antimicrobial resistance genes (ARGs). Studies of the gut microbiome have found an association between age and increasing antimicrobial resistance gene (ARG) burden, however corollary studies in the lung microbiome remain absent, despite the respiratory tract representing one of the most clinically significant sites for drug resistant infections. We performed a prospective, multicenter observational study of 261 children and 88 adults with acute respiratory failure, ranging in age from 31 days to ≥ 89 years, admitted to intensive care units in the United States. We performed RNA sequencing on tracheal aspirates collected within 72 hours of intubation, and evaluated age-related differences in detectable ARG expression in the lung microbiome as a primary outcome. Secondary outcomes included number and classes of ARGs detected, proportion of patients with an ARG class, and composition of the lung microbiome. Multivariable logistic regression models (adults vs children) or continuous age (years) were adjusted for sex, race/ethnicity, LRTI status, and days from intubation to specimen collection. Detection of ARGs was significantly higher in adults compared with children after adjusting for sex, race/ethnicity, LRTI diagnosis, and days from intubation to specimen collection (adjusted odds ratio (aOR): 2.16, 95% confidence interval (CI): 1.10-4.22). A greater proportion of adults compared with children had beta-lactam ARGs (31% (CI: 21-41%) vs 13% (CI: 10-18%)), aminoglycoside ARGs (20% (CI: 13-30%) vs 2% (CI: 0.6-4%)), and tetracycline ARGs (14% (CI: 7-23%) vs 3% (CI: 1-5%)). Adults ≥70 years old had the highest proportion of these three ARG classes. The total bacterial abundance of the lung microbiome increased with age, and microbiome alpha diversity varied with age. Taxonomic composition of the lung microbiome, measured by Bray Curtis dissimilarity index, differed between adults and children (p = 0.003). The association between age and increased ARG detection remained significant after additionally including lung microbiome total bacterial abundance and alpha diversity in the multivariable logistic regression model (aOR: 2.38, (CI: 1.25-4.54)). Furthermore, this association remained robust when modeling age as a continuous variable (aOR: 1.02, (CI: 1.01-1.03) per year of age). Taken together, our results demonstrate that age is an independent risk factor for ARG detection in the lower respiratory tract microbiome. These data shape our understanding of the lung resistome in critically ill patients across the lifespan, which may have implications for clinical management and global public health.

Anti-nucleocapsid antibody levels and pulmonary comorbid conditions are linked to post-COVID-19 syndrome.

BACKGROUNDProlonged symptoms after SARS-CoV-2 infection are well documented. However, which factors influence development of long-term symptoms, how symptoms vary across ethnic groups, and whether long-term symptoms correlate with biomarkers are points that remain elusive.METHODSAdult SARS-CoV-2 reverse transcription PCR-positive (RT-PCR-positive) patients were recruited at Stanford from March 2020 to February 2021. Study participants were seen for in-person visits at diagnosis and every 1-3 months for up to 1 year after diagnosis; they completed symptom surveys and underwent blood draws and nasal swab collections at each visit.RESULTSOur cohort (n = 617) ranged from asymptomatic to critical COVID-19 infections. In total, 40% of participants reported at least 1 symptom associated with COVID-19 six months after diagnosis. Median time from diagnosis to first resolution of all symptoms was 44 days; median time from diagnosis to sustained symptom resolution with no recurring symptoms for 1 month or longer was 214 days. Anti-nucleocapsid IgG level in the first week after positive RT-PCR test and history of lung disease were associated with time to sustained symptom resolution. COVID-19 disease severity, ethnicity, age, sex, and remdesivir use did not affect time to sustained symptom resolution.CONCLUSIONWe found that all disease severities had a similar risk of developing post-COVID-19 syndrome in an ethnically diverse population. Comorbid lung disease and lower levels of initial IgG response to SARS-CoV-2 nucleocapsid antigen were associated with longer symptom duration.TRIAL REGISTRATIONClinicalTrials.gov, NCT04373148.FUNDINGNIH UL1TR003142 CTSA grant, NIH U54CA260517 grant, NIEHS R21 ES03304901, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative, Sunshine Foundation, Crown Foundation, and Parker Foundation.

Unique roles of vaginal Megasphaera phylotypes in reproductive health.

The composition of the human vaginal microbiome has been extensively studied and is known to influence reproductive health. However, the functional roles of individual taxa and their contributions to negative health outcomes have yet to be well characterized. Here, we examine two vaginal bacterial taxa grouped within the genus Megasphaera that have been previously associated with bacterial vaginosis (BV) and pregnancy complications. Phylogenetic analyses support the classification of these taxa as two distinct species. These two phylotypes, Megasphaera phylotype 1 (MP1) and Megasphaera phylotype 2 (MP2), differ in genomic structure and metabolic potential, suggestive of differential roles within the vaginal environment. Further, these vaginal taxa show evidence of genome reduction and changes in DNA base composition, which may be common features of host dependence and/or adaptation to the vaginal environment. In a cohort of 3870 women, we observed that MP1 has a stronger positive association with bacterial vaginosis whereas MP2 was positively associated with trichomoniasis. MP1, in contrast to MP2 and other common BV-associated organisms, was not significantly excluded in pregnancy. In a cohort of 52 pregnant women, MP1 was both present and transcriptionally active in 75.4 % of vaginal samples. Conversely, MP2 was largely absent in the pregnant cohort. This study provides insight into the evolutionary history, genomic potential and predicted functional role of two clinically relevant vaginal microbial taxa.

SARS-CoV-2 variants associated with vaccine breakthrough in the Delaware Valley through summer 2021.

The severe acute respiratory coronavirus-2 (SARS-CoV-2) is the cause of the global outbreak of COVID-19. Evidence suggests that the virus is evolving to allow efficient spread through the human population, including vaccinated individuals. Here we report a study of viral variants from surveillance of the Delaware Valley, including the city of Philadelphia, and variants infecting vaccinated subjects. We sequenced and analyzed complete viral genomes from 2621 surveillance samples from March 2020 to September 2021 and compared them to genome sequences from 159 vaccine breakthroughs. In the early spring of 2020, all detected variants were of the B.1 and closely related lineages. A mixture of lineages followed, notably including B.1.243 followed by B.1.1.7 (alpha), with other lineages present at lower levels. Later isolations were dominated by B.1.617.2 (delta) and other delta lineages; delta was the exclusive variant present by the last time sampled. To investigate whether any variants appeared preferentially in vaccine breakthroughs, we devised a model based on Bayesian autoregressive moving average logistic multinomial regression to allow rigorous comparison. This revealed that B.1.617.2 (delta) showed three-fold enrichment in vaccine breakthrough cases (odds ratio of 3; 95% credible interval 0.89-11). Viral point substitutions could also be associated with vaccine breakthroughs, notably the N501Y substitution found in the alpha, beta and gamma variants (odds ratio 2.04; 95% credible interval of 1.25-3.18). This study thus provides a detailed picture of viral evolution in the Delaware Valley and a geographically matched analysis of vaccine breakthroughs; it also introduces a rigorous statistical approach to interrogating enrichment of viral variants.

Signatures of COVID-19 Severity and Immune Response in the Respiratory Tract Microbiome.

Viral infection of the respiratory tract can be associated with propagating effects on the airway microbiome, and microbiome dysbiosis may influence viral disease. Here, we investigated the respiratory tract microbiome in coronavirus disease 2019 (COVID-19) and its relationship to disease severity, systemic immunologic features, and outcomes. We examined 507 oropharyngeal, nasopharyngeal, and endotracheal samples from 83 hospitalized COVID-19 patients as well as non-COVID patients and healthy controls. Bacterial communities were interrogated using 16S rRNA gene sequencing, and the commensal DNA viruses Anelloviridae and Redondoviridae were quantified by qPCR. We found that COVID-19 patients had upper respiratory microbiome dysbiosis and greater change over time than critically ill patients without COVID-19. Oropharyngeal microbiome diversity at the first time point correlated inversely with disease severity during hospitalization. Microbiome composition was also associated with systemic immune parameters in blood, as measured by lymphocyte/neutrophil ratios and immune profiling of peripheral blood mononuclear cells. Intubated patients showed patient-specific lung microbiome communities that were frequently highly dynamic, with prominence of Staphylococcus. Anelloviridae and Redondoviridae showed more frequent colonization and higher titers in severe disease. Machine learning analysis demonstrated that integrated features of the microbiome at early sampling points had high power to discriminate ultimate level of COVID-19 severity. Thus, the respiratory tract microbiome and commensal viruses are disturbed in COVID-19 and correlate with systemic immune parameters, and early microbiome features discriminate disease severity. Future studies should address clinical consequences of airway dysbiosis in COVID-19, its possible use as biomarkers, and the role of bacterial and viral taxa identified here in COVID-19 pathogenesis. IMPORTANCE COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of the respiratory tract, results in highly variable outcomes ranging from minimal illness to death, but the reasons for this are not well understood. We investigated the respiratory tract bacterial microbiome and small commensal DNA viruses in hospitalized COVID-19 patients and found that each was markedly abnormal compared to that in healthy people and differed from that in critically ill patients without COVID-19. Early airway samples tracked with the level of COVID-19 illness reached during hospitalization, and the airway microbiome also correlated with immune parameters in blood. These findings raise questions about the mechanisms linking SARS-CoV-2 infection and other microbial inhabitants of the airway, including whether the microbiome might regulate severity of COVID-19 disease and/or whether early microbiome features might serve as biomarkers to discriminate disease severity.

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Tracking.

BACKGROUND: High-sensitivity severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen assays are desirable to mitigate false negative results. Limited data are available to quantify and track SARS-CoV-2 antigen burden in respiratory samples from different populations. METHODS: We developed the Microbubbling SARS-CoV-2 Antigen Assay (MSAA) with smartphone readout, with a limit of detection of 0.5 pg/mL (10.6 fmol/L) nucleocapsid antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs. We developed a computer vision and machine learning-based automatic microbubble image classifier to accurately identify positives and negatives and quantified and tracked antigen dynamics in intensive care unit coronavirus disease 2019 (COVID-19) inpatients and immunocompromised COVID-19 patients. RESULTS: Compared to qualitative reverse transcription-polymerase chain reaction methods, the MSAA demonstrated a positive percentage agreement of 97% (95% CI 92%-99%) and a negative percentage agreement of 97% (95% CI 94%-100%) in a clinical validation study with 372 residual clinical NP swabs. In immunocompetent individuals, the antigen positivity rate in swabs decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity. Antigen was detected for longer and variable periods of time in immunocompromised patients with hematologic malignancies. Total microbubble volume, a quantitative marker of antigen burden, correlated inversely with cycle threshold values and days-after-symptom-onset. Viral sequence variations were detected in patients with long duration of high antigen burden. CONCLUSIONS: The MSAA enables sensitive and specific detection of acute infections and quantification and tracking of antigen burden and may serve as a screening method in longitudinal studies to identify patients who are likely experiencing active rounds of ongoing replication and warrant close viral sequence monitoring.

Detection of SARS-CoV-2 RNA using RT-LAMP and molecular beacons.

BACKGROUND: Rapid spread of SARS-CoV-2 has led to a global pandemic, resulting in the need for rapid assays to allow diagnosis and prevention of transmission. Reverse transcription-polymerase chain reaction (RT-PCR) provides a gold standard assay for SARS-CoV-2 RNA, but instrument costs are high and supply chains are potentially fragile, motivating interest in additional assay methods. Reverse transcription and loop-mediated isothermal amplification (RT-LAMP) provides an alternative that uses orthogonal and often less expensive reagents without the need for thermocyclers. The presence of SARS-CoV-2 RNA is typically detected using dyes to report bulk amplification of DNA; however, a common artifact is nonspecific DNA amplification, which complicates detection. RESULTS: Here we describe the design and testing of molecular beacons, which allow sequence-specific detection of SARS-CoV-2 genomes with improved discrimination in simple reaction mixtures. To optimize beacons for RT-LAMP, multiple locked nucleic acid monomers were incorporated to elevate melting temperatures. We also show how beacons with different fluorescent labels can allow convenient multiplex detection of several amplicons in "single pot" reactions, including incorporation of a human RNA LAMP-BEAC assay to confirm sample integrity. Comparison of LAMP-BEAC and RT-qPCR on clinical saliva samples showed good concordance between assays. To facilitate implementation, we developed custom polymerases for LAMP-BEAC and inexpensive purification procedures, which also facilitates increasing sensitivity by increasing reaction volumes. CONCLUSIONS: LAMP-BEAC thus provides an affordable and simple SARS-CoV-2 RNA assay suitable for population screening; implementation of the assay has allowed robust screening of thousands of saliva samples per week.

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Dynamics Tracking.

Background: Little is known about the dynamics of SARS-CoV-2 antigen burden in respiratory samples in different patient populations at different stages of infection. Current rapid antigen tests cannot quantitate and track antigen dynamics with high sensitivity and specificity in respiratory samples. Methods: We developed and validated an ultra-sensitive SARS-CoV-2 antigen assay with smartphone readout using the Microbubbling Digital Assay previously developed by our group, which is a platform that enables highly sensitive detection and quantitation of protein biomarkers. A computer vision-based algorithm was developed for microbubble smartphone image recognition and quantitation. A machine learning-based classifier was developed to classify the smartphone images based on detected microbubbles. Using this assay, we tracked antigen dynamics in serial swab samples from COVID patients hospitalized in ICU and immunocompromised COVID patients. Results: The limit of detection (LOD) of the Microbubbling SARS-CoV-2 Antigen Assay was 0.5 pg/mL (10.6 fM) recombinant nucleocapsid (N) antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs, comparable to many rRT-PCR methods. The assay had high analytical specificity towards SARS-CoV-2. Compared to EUA-approved rRT-PCR methods, the Microbubbling Antigen Assay demonstrated a positive percent agreement (PPA) of 97% (95% confidence interval (CI), 92-99%) in symptomatic individuals within 7 days of symptom onset and positive SARS-CoV-2 nucleic acid results, and a negative percent agreement (NPA) of 97% (95% CI, 94-100%) in symptomatic and asymptomatic individuals with negative nucleic acid results. Antigen positivity rate in NP swabs gradually decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity of the same samples. The computer vision and machine learning-based automatic microbubble image classifier could accurately identify positives and negatives, based on microbubble counts and sizes. Total microbubble volume, a potential marker of antigen burden, correlated inversely with Ct values and days-after-symptom-onset. Antigen was detected for longer periods of time in immunocompromised patients with hematologic malignancies, compared to immunocompetent individuals. Simultaneous detectable antigens and nucleic acids may indicate the presence of replicating viruses in patients with persistent infections. Conclusions: The Microbubbling SARS-CoV-2 Antigen Assay enables sensitive and specific detection of acute infections, and quantitation and tracking of antigen dynamics in different patient populations at various stages of infection. With smartphone compatibility and automated image processing, the assay is well-positioned to be adapted for point-of-care diagnosis and to explore the clinical implications of antigen dynamics in future studies.

Signatures of COVID-19 severity and immune response in the respiratory tract microbiome.

Rationale: Viral infection of the respiratory tract can be associated with propagating effects on the airway microbiome, and microbiome dysbiosis may influence viral disease. Objective: To define the respiratory tract microbiome in COVID-19 and relationship disease severity, systemic immunologic features, and outcomes. Methods and Measurements: We examined 507 oropharyngeal, nasopharyngeal and endotracheal samples from 83 hospitalized COVID-19 patients, along with non-COVID patients and healthy controls. Bacterial communities were interrogated using 16S rRNA gene sequencing, commensal DNA viruses Anelloviridae and Redondoviridae were quantified by qPCR, and immune features were characterized by lymphocyte/neutrophil (L/N) ratios and deep immune profiling of peripheral blood mononuclear cells (PBMC). Main Results: COVID-19 patients had upper respiratory microbiome dysbiosis, and greater change over time than critically ill patients without COVID-19. Diversity at the first time point correlated inversely with disease severity during hospitalization, and microbiome composition was associated with L/N ratios and PBMC profiles in blood. Intubated patients showed patient-specific and dynamic lung microbiome communities, with prominence of Staphylococcus. Anelloviridae and Redondoviridae showed more frequent colonization and higher titers in severe disease. Machine learning analysis demonstrated that integrated features of the microbiome at early sampling points had high power to discriminate ultimate level of COVID-19 severity. Conclusions: The respiratory tract microbiome and commensal virome are disturbed in COVID-19, correlate with systemic immune parameters, and early microbiome features discriminate disease severity. Future studies should address clinical consequences of airway dysbiosis in COVID-19, possible use as biomarkers, and role of bacterial and viral taxa identified here in COVID-19 pathogenesis.

SARS-CoV-2 Genomic Variation in Space and Time in Hospitalized Patients in Philadelphia.

The severe acute respiratory coronavirus 2 (SARS-CoV-2) is the cause of the global outbreak of COVID-19. The epidemic accelerated in Philadelphia, PA, in the spring of 2020, with the city experiencing a first peak of infections on 15 April, followed by a decline through midsummer. Here, we investigate spread of the epidemic in the first wave in Philadelphia using full-genome sequencing of 52 SARS-CoV-2 samples obtained from 27 hospitalized patients collected between 30 March and 17 July 2020. Sequences most commonly resembled lineages circulating at earlier times in New York, suggesting transmission primarily from this location, though a minority of Philadelphia genomes matched sequences from other sites, suggesting additional introductions. Multiple genomes showed even closer matches to other Philadelphia isolates, suggestive of ongoing transmission within Philadelphia. We found that all of our isolates contained the D614G substitution in the viral spike and belong to lineages variously designated B.1, Nextstrain clade 20A or 20C, and GISAID clade G or GH. There were no viral sequence polymorphisms detectably associated with disease outcome. For some patients, genome sequences were determined longitudinally or concurrently from multiple body sites. In both cases, some comparisons showed reproducible polymorphisms, suggesting initial seeding with multiple variants and/or accumulation of polymorphisms after infection. These results thus provide data on the sources of SARS-CoV-2 infection in Philadelphia and begin to explore the dynamics within hospitalized patients.IMPORTANCE Understanding how SARS-CoV-2 spreads globally and within infected individuals is critical to the development of mitigation strategies. We found that most lineages in Philadelphia had resembled sequences from New York, suggesting infection primarily but not exclusively from this location. Many genomes had even nearer neighbors within Philadelphia, indicating local spread. Multiple genome sequences were available for some subjects and in a subset of cases could be shown to differ between time points and body sites within an individual, indicating heterogeneous viral populations within individuals and raising questions on the mechanisms responsible. There was no evidence that different lineages were associated with different outcomes in patients, emphasizing the importance of individual-specific vulnerability.

SARS-CoV-2 Variants Associated with Vaccine Breakthrough in the Delaware Valley through Summer 2021.

The severe acute respiratory coronavirus-2 (SARS-CoV-2) is the cause of the global outbreak of COVID-19. Evidence suggests that the virus is evolving to allow efficient spread through the human population, including vaccinated individuals. Here, we report a study of viral variants from surveillance of the Delaware Valley, including the city of Philadelphia, and variants infecting vaccinated subjects. We sequenced and analyzed complete viral genomes from 2621 surveillance samples from March 2020 to September 2021 and compared them to genome sequences from 159 vaccine breakthroughs. In the early spring of 2020, all detected variants were of the B.1 and closely related lineages. A mixture of lineages followed, notably including B.1.243 followed by B.1.1.7 (alpha), with other lineages present at lower levels. Later isolations were dominated by B.1.617.2 (delta) and other delta lineages; delta was the exclusive variant present by the last time sampled. To investigate whether any variants appeared preferentially in vaccine breakthroughs, we devised a model based on Bayesian autoregressive moving average logistic multinomial regression to allow rigorous comparison. This revealed that B.1.617.2 (delta) showed 3-fold enrichment in vaccine breakthrough cases (odds ratio of 3; 95% credible interval 0.89-11). Viral point substitutions could also be associated with vaccine breakthroughs, notably the N501Y substitution found in the alpha, beta and gamma variants (odds ratio 2.04; 95% credible interval of1.25-3.18). This study thus overviews viral evolution and vaccine breakthroughs in the Delaware Valley and introduces a rigorous statistical approach to interrogating enrichment of breakthrough variants against a changing background. IMPORTANCE SARS-CoV-2 vaccination is highly effective at reducing viral infection, hospitalization and death. However, vaccine breakthrough infections have been widely observed, raising the question of whether particular viral variants or viral mutations are associated with breakthrough. Here, we report analysis of 2621 surveillance isolates from people diagnosed with COVID-19 in the Delaware Valley in southeastern Pennsylvania, allowing rigorous comparison to 159 vaccine breakthrough case specimens. Our best estimate is a 3-fold enrichment for some lineages of delta among breakthroughs, and enrichment of a notable spike substitution, N501Y. We introduce statistical methods that should be widely useful for evaluating vaccine breakthroughs and other viral phenotypes.

Considerations for the Safe Operation of Schools During the Coronavirus Pandemic.

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, providing safe in-person schooling has been a dynamic process balancing evolving community disease burden, scientific information, and local regulatory requirements with the mandate for education. Considerations include the health risks of SARS-CoV-2 infection and its post-acute sequelae, the impact of remote learning or periods of quarantine on education and well-being of children, and the contribution of schools to viral circulation in the community. The risk for infections that may occur within schools is related to the incidence of SARS-CoV-2 infections within the local community. Thus, persistent suppression of viral circulation in the community through effective public health measures including vaccination is critical to in-person schooling. Evidence suggests that the likelihood of transmission of SARS-CoV-2 within schools can be minimized if mitigation strategies are rationally combined. This article reviews evidence-based approaches and practices for the continual operation of in-person schooling.

The vaginal microbiome and preterm birth.

The incidence of preterm birth exceeds 10% worldwide. There are significant disparities in the frequency of preterm birth among populations within countries, and women of African ancestry disproportionately bear the burden of risk in the United States. In the present study, we report a community resource that includes 'omics' data from approximately 12,000 samples as part of the integrative Human Microbiome Project. Longitudinal analyses of 16S ribosomal RNA, metagenomic, metatranscriptomic and cytokine profiles from 45 preterm and 90 term birth controls identified harbingers of preterm birth in this cohort of women predominantly of African ancestry. Women who delivered preterm exhibited significantly lower vaginal levels of Lactobacillus crispatus and higher levels of BVAB1, Sneathia amnii, TM7-H1, a group of Prevotella species and nine additional taxa. The first representative genomes of BVAB1 and TM7-H1 are described. Preterm-birth-associated taxa were correlated with proinflammatory cytokines in vaginal fluid. These findings highlight new opportunities for assessment of the risk of preterm birth.